Particle filtering has drawn more attention recently due to its superior performance in nonlinear and non-Gaussian problems

which uses an observation model to describe the interested target and its performance depends strongly on the observation model. A novel particle filter algorithm has been proposed for head tracking

which focuses on finding a solution to the problem that the feature of the tracked object does not always match the particle observation model with changing time. In brief

it unifies the difference of the tracked object features with the particle observation models

and thus it forms a group of particles with different models to track the target. Based on the changing displays in the feature cues of the head with the rotation

particles with different observation models convert alternately in the tracking process. To evaluate the novel head tracker performance

some real sequences is tested and some results are shown that the new tracker is robust to the rotation of head in a cluttered background and has better tracking precision than the standard particle filter.